Antwerp, Belgium - November 20th, 2025

The retail power balance has fundamentally shifted. As generative AI transforms shopping from manual selection to algorithmic curation, retailers globally are evolving from distribution channels into brand-makers with unprecedented control over consumer choice. With AI-driven traffic to retail sites growing 4,700% year-over-year according to Adobe Analytics, and private label sales projected to reach $277 billion in the US alone with similar trajectories across Europe and Asia, traditional brands face an existential question: How do you stay visible when an AI assistant, not the shopper, builds the basket?

For years, brands poured millions into trying to win the shelf. They chased better data, sharper packaging, stronger claims, and marketing built to resonate. But getting that edge took time. Teams spent months waiting for research cycles to finish before any insight became useful, and by the time the data finally arrived there was a chance that the market had moved on.

Today's retail giants from Walmart and Target in America to Carrefour in Europe and Alibaba in Asia operate intelligence systems that capture every click, cart addition, and abandoned purchase. They know which claims convert, which price points trigger purchases, and which product descriptions resonate with specific demographics, all in real-time.
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It didn’t take long for retailers to use this mountain of data to their advantage. Private labels grew from generic alternatives into sophisticated brand portfolios. Consider the velocity: according to Talk Business & Politics data, Walmart's Bettergoods and Target's Dealworthy brands increased sales volume by 200% in their first year. In Europe, retailers like Albert Heijn's Terra line and Jumbo's private portfolio compete directly with premium nationals.

This advantage will exponentially grow with the arrival of GenAI. Walmart is already pushing the frontier, showing how an AI assistant can reshape the online shopping experience. People have been buying groceries online for a while now, but they still need to click every item as if they’re filling a physical basket. And that’s exactly where things start to shift, because this tiny moment of manual choice is often the difference between one brand winning and another losing

Tim explains the implications: "A consumer says, 'I need healthy snacks for my kids' lunch boxes.' The AI doesn't show them a shelf of options; it selects products based on the retailer's algorithm. If your brand isn't optimized for that algorithm, you're invisible."

This small shift gives retailers the power to decide which products their AI recommends, handing them unprecedented influence over what people end up buying. That puts many brands at risk of being overshadowed by retailer-owned private labels. While this sounds dystopian for some, the data shows otherwise. By July 2025, Adobe found that 38% of consumers had already used generative AI for shopping, with 52% planning to do so within the year.

When Walmart announced its OpenAI partnership for "Instant Checkout" functionality, a model other retailers are already racing to copy, they painted a future that should concern every brand manager: customers will simply chat with AI assistants about their needs, and products will appear in their carts without any browsing, comparison, or brand consideration. This signals a deep shift in loyalty dynamics.

At Made, we've been tracking this transformation across continents, working with brands from diverse markets to understand not just the threat, but the opportunity. As Tim Van den Bergh emphasizes: "The brands that will win are those that embrace a fundamental truth: AI doesn't eliminate the need for brands, it redefines what makes them valuable."

We've identified three critical strategies that forward-thinking companies are already implementing:

Oréal has partnered with multiple AI platforms to ensure their products surface correctly in voice and chat searches across markets. They've invested millions in structuring product data including ingredients, benefits, and use cases in ways AI systems can parse and prioritize. Similarly, Unilever is creating what they call "digital twins" of products, rich data profiles that ensure their brands remain discoverable whether a consumer shops in London, Lagos, or Los Angeles.

Brands like Nike and Adidas have shifted focus from competing on shelves to creating unmissable cultural moments. Nike's exclusive drops through their SNKRS app generate specific product requests that override AI recommendations. In China, Perfect Diary built a billion-dollar beauty brand by combining AI-powered personalization with influencer experiences that consumers actively seek out. These brands understand that when consumers request products by name, they bypass algorithmic gatekeepers entirely

Procter & Gamble has pioneered direct data partnerships with major retailers globally, co-creating AI shopping experiences that benefit both parties. They're not fighting the algorithm; they're helping shape it. In Europe, Nestlé works directly with retailers on "algorithmic category management," ensuring their products meet the specific parameters each retailer's AI prioritizes.

Consider Oatly, which transformed oat milk from commodity to cultural phenomenon by creating such distinctive brand language that consumers specifically request it, whether shopping in Stockholm or San Francisco. Or look at how Korean beauty brands like Glow Recipe built global followings by combining unique formulations with social storytelling that makes their products AI-proof. Consumers don't want "a moisturizer," they want "that plum moisturizer from TikTok."

The opportunity extends beyond defense to offense. Brands that master AI optimization while maintaining human connection can actually strengthen their position. The key is what we at Made call "algorithmic brand architecture": building brands that excel in both human and machine discovery. This means investing in rich product data and structured metadata frameworks, while simultaneously creating cultural relevance that drives specific requests.

We've developed a comprehensive framework for brands navigating this transition, focusing on strategic transformation rather than tactical adjustments. Our approach centers on three pillars of AI growth strategy:

Strategic portfolio evolution. Where we help brands reimagine their entire product portfolio through the lens of AI discoverability. By identifying which product attributes drive both algorithmic preference and human appeal.

Long-term narrative development. We identifying the unique brand codes that create sustained differentiation from private labels, building partnership strategies with retailers that position your brand as essential to their AI shopping experiences rather than replaceable.

Data architecture and metadata strategy. We help brands build structured, AI-ready product data ecosystems. Ensuring that your products are discoverable and prioritized when consumers interact with AI assistants.

This is ultimately a story of strategic evolution, not extinction. Just as brands adapted to supermarkets, e-commerce, and mobile shopping through fundamental business model innovation, they'll adapt to AI-mediated commerce. But only if they start with the right strategic foundation today.

Let's explore how your brand can build sustainable competitive advantage in an algorithm-driven marketplace. Book a 30-minute discovery session with me to discuss your strategic position and identify the portfolio innovations and data strategies that will define your future success.